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January 2026
MCP Server

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MCP Server for Datasets and Workflows

Introducing the Tilebox MCP server, which provides tools for AI agents to access and interact with Tilebox datasets and workflows.
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November 2025

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Job List View: Complete Redesign and Filtering Improvements

Job List View Improvements
The job list view in the Tilebox Console has been completely redesigned:
  • Infinite scrolling for long job lists
  • Improved filtering and search
    • Filter by state
    • Filter by automation
    • Filter by time range
    • Filter by name
    • Or an arbitrary combination of the above
  • Better readability and organization
  • Added progress indicators
  • Added execution stats
  • The same new filter options are also available in our Language SDKs
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October 2025

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Spatio-Temporal Explorer Redesign

Console Improvements
The dataset explorer in the Tilebox Console has received a major upgrade for spatio-temporal datasets:
  • Overhaul of the Explorer view for Spatio-temporal datasets
  • Display datapoint footprints directly on the map
  • Display thumbnails and quicklooks of datapoints directly in the Console
  • Added code snippet for storage access to the Export as Code dialog
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September 2025

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Progress Indicators

Progress Indicators
User defined progress indicators for jobs are now available. See the progress documentation for more details.
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May 2025

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Go Client

Tilebox Go Client
Excited to announce the release of the Tilebox Go client!Features
  • Datasets client
  • Statically typed dataset types
  • CLI to generate dataset types
  • Workflows client
  • Go task runners
To get started, check out the Go SDK documentation.
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April 2025

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Spatio-Temporal datasets

Spatio-Temporal Datasets
Spatio-temporal datasets are officially out, fully supported in all languages and available as a category to create in custom datasets!The core problems that spatio-temporal datasets solve are
  • finding relevant data quickly (e.g. all Sentinel 2 granules along the US coastline, last year),
  • storing auxiliary geographically coded data (e.g. weather station data, ground truth data),
  • cataloging higher level data and results
Here’s a short video on performance and core capabilities.We’re excited about this as cataloging has until now been an unsexy but hard problem, and it’s great to finally have a solution out thereMore information
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March 2025

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Custom Datasets

Custom datasets
Create your own custom datasets!
  • statically typed
  • with clients in Python and Go
Use it to organize anything from telemetry, raw payload metadata, auxiliary sensor data, configuration data, or internal data catalogs.Quickstart
  1. Specify the data type in the Console
  2. Create a collection
  3. Use client.ingest() to ingest a xarray.Dataset or pandas.DataFrame
  4. Query away!
For detailed instructions, check out the Creating a dataset how-to guide.